Instructions to use allenai/OLMo-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/OLMo-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="allenai/OLMo-7B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allenai/OLMo-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/OLMo-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/OLMo-7B-Instruct

SGLang

How to use allenai/OLMo-7B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "allenai/OLMo-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "allenai/OLMo-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/OLMo-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/OLMo-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/allenai/OLMo-7B-Instruct
```

jacobmorrison commited on Feb 27, 2024

Commit

2d50cde

verified ·

1 Parent(s): 53f06b7

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -85,14 +85,17 @@ import hf_olmo
 from transformers import AutoModelForCausalLM, AutoTokenizer
 olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-Instruct")
-message = ["Language modeling is "]
-inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
 # optional verifying cuda
 # inputs = {k: v.to('cuda') for k,v in inputs.items()}
 # olmo = olmo.to('cuda')
-response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
 print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
->> 'Language modeling is the first step to build natural language generation...'
 ```
 Alternatively, with the pipeline abstraction:
 ```python
@@ -100,8 +103,8 @@ import hf_olmo
 from transformers import pipeline
 olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B-Instruct")
-print(olmo_pipe("Language modeling is "))
->> 'Language modeling is a branch of natural language processing that aims to...'
 ```
 Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).

 from transformers import AutoModelForCausalLM, AutoTokenizer
 olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-Instruct")
+chat = [
+    { "role": "user", "content": "What is language modeling?" },
+]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
 # optional verifying cuda
 # inputs = {k: v.to('cuda') for k,v in inputs.items()}
 # olmo = olmo.to('cuda')
+response = olmo.generate(input_ids=inputs.to(olmo.device), max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
 print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
+>> '<|user|>\nWhat is language modeling?\n<|assistant|>\nLanguage modeling is a type of natural language processing (NLP) task or machine learning task that...'
 ```
 Alternatively, with the pipeline abstraction:
 ```python
 from transformers import pipeline
 olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B-Instruct")
+print(olmo_pipe("What is language modeling?"))
+>> '[{'generated_text': 'What is language modeling?\nLanguage modeling is a type of natural language processing (NLP) task...'}]'
 ```
 Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).